Model based prediction in a critically sampled filterbank

ABSTRACT

The present document relates to audio source coding systems. In particular, the present document relates to audio source coding systems which make use of linear prediction in combination with a filterbank. A method for estimating a first sample ( 615 ) of a first subband signal in a first subband of an audio signal is described. The first subband signal of the audio signal is determined using an analysis filterbank ( 612 ) comprising a plurality of analysis filters which provide a plurality of subband signals in a plurality of subbands from the audio signal, respectively. The method comprises determining a model parameter ( 613 ) of a signal model; determining a prediction coefficient to be applied to a previous sample ( 614 ) of a first decoded subband signals derived from the first subband signal, based on the signal model, based on the model parameter ( 613 ) and based on the analysis filterbank ( 612 ); wherein a time slot of the previous sample ( 614 ) is prior to a time slot of the first sample ( 615 ); and determining an estimate of the first sample ( 615 ) by applying the prediction coefficient to the previous sample ( 614 ).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application from U.S. patentapplication Ser. No. 14/655,037 filed Jun. 23, 2015 and now allowedwhich was a U.S. 371 national phase of PCT/EP2014/050139 filed Jan. 7,2014 claiming priority to U.S. patent application Ser. No. 61/750,052filed Jan. 8, 2013 and U.S. patent application Ser. No. 61/875,528 filedSep. 9, 2013 which are hereby incorporated by reference in theirentirety.

TECHNICAL FIELD

The present document relates to audio source coding systems. Inparticular, the present document relates to audio source coding systemswhich make use of linear prediction in combination with a filterbank.

BACKGROUND

There are two important signal processing tools applied in systems forsource coding of audio signals, namely critically sampled filterbanksand linear prediction. Critically sampled filterbanks (e.g. modifieddiscrete cosine transform, MDCT, based filterbanks) enable direct accessto time-frequency representations where perceptual irrelevancy andsignal redundancy can be exploited. Linear prediction enables theefficient source modeling of audio signals, in particular of speechsignals. The combination of the two tools, i.e. the use of prediction inthe subbands of a filterbank, has mainly been used for high bit rateaudio coding. For low bit rate coding, a challenge with prediction inthe subbands is to keep the cost (i.e. the bit rate) for the descriptionof the predictors low. Another challenge is to control the resultingnoise shaping of the prediction error signal obtained by a subbandpredictor.

For the challenge of encoding the description of the subband predictorin a bit-efficient manner, a possible path is to estimate the predictorfrom previously decoded portions of the audio signal and to therebyavoid the cost of a predictor description altogether. If the predictorcan be determined from previously decoded portions of the audio signal,the predictor can be determined at the encoder and at the decoder,without the need of transmitting a predictor description from theencoder to the decoder. This scheme is referred to as a backwardsadaptive prediction scheme. However, the backwards adaptive predictionscheme typically degrades significantly when the bit rate of the encodedaudio signal decreases. An alternative or additional path to theefficient encoding of a subband predictor is to identify a more naturalpredictor description, e.g. a description which exploits the inherentstructure of the to-be-encoded audio signal. For instance, low bit ratespeech coding typically applies a forward adaptive scheme based on acompact representation of a short term predictor (exploiting short termcorrelations) and a long time predictor (exploiting long termcorrelations due to an underlying pitch of the speech signal).

For the challenge of controlling the noise shaping of the predictionerror signal, it is observed that while the noise shaping of a predictormay be well controlled inside of a subband, the final output audiosignal of the encoder typically exhibits alias artifacts (except foraudio signals exhibiting a substantially flat spectral noise shape).

An important case of a subband predictor is the implementation of longterm prediction in a filterbank with overlapping windows. A long termpredictor typically exploits the redundancies in periodic and nearperiodic audio signals (such as speech signals exhibiting an inherentpitch), and may be described with a single or a low number of predictionparameters. The long term predictor may be defined in continuous time bymeans of a delay which reflects the periodicity of the audio signal.When this delay is large compared to the length of the filterbankwindow, the long term predictor can be implemented in the discrete timedomain by means of a shift or a fractional delay and may be convertedback into a causal predictor in the subband domain. Such a long termpredictor typically does not exhibit alias artifacts, but there is asignificant penalty in computational complexity caused by the need foradditional filterbank operations for the conversion from the time domainto the subband domain. Furthermore, the approach of determining thedelay in the time domain and of converting the delay into a subbandpredictor is not applicable for the case where the period of theto-be-encoded audio signal is comparable or smaller than the filterbankwindow size.

The present document addresses the above mentioned shortcomings ofsubband prediction. In particular, the present document describesmethods and systems which allow for a bit-rate efficient description ofsubband predictors and/or which allow for a reduction of alias artifactscaused by subband predictors. In particular, the method and systemsdescribed in the present document enable the implementation of low bitrate audio coders using subband prediction, which cause a reduced levelof aliasing artifacts.

SUMMARY

The present document describes methods and systems which improve thequality of audio source coding employing prediction in the subbanddomain of a critically sampled filterbank. The methods and systems maymake use of a compact description of subband predictors, wherein thedescription is based on signal models. Alternatively or in addition, themethods and systems may make use of an efficient implementation ofpredictors directly in the subband domain. Alternatively or in addition,the methods and systems may make use of cross subband predictor terms,as described in the present document, to allow for a reduction of aliasartifacts.

As outlined in the present document, the compact description of subbandpredictors may comprise the frequency of a sinusoid, the period of aperiodical signal, a slightly inharmonic spectrum as encountered for thevibration of a stiff string, and/or a multitude of pitches for apolyphonic signal. It is shown that for the case of a long termpredictor, the periodical signal model provides high quality causalpredictors for a range of lag parameters (or delays) that includesvalues which are shorter and/or longer than the window size of thefilterbank. This means that a periodical signal model may be used toimplement a long term subband predictor in an efficient manner. Aseamless transition is provided from sinusoidal model based predictionto the approximation of an arbitrary delay.

The direct implementation of predictors in the subband domain enablesexplicit access to perceptual characteristics of the producedquantization distortions. Furthermore, the implementation of predictorsin the subband domain enables access to numerical properties such as theprediction gain and the dependence of the predictors on the parameters.For instance, a signal model based analysis can reveal that theprediction gain is only significant in a subset of the consideredsubbands, and the variation of the predictor coefficients as a functionof the parameter chosen for transmission can be helpful in the design ofparameter formats, as well as efficient encoding algorithms. Moreover,the computational complexity may be reduced significantly compared topredictor implementations that rely on the use of algorithms operatingboth in the time domain and in the subband domain. In particular, themethods and systems described in the present document may be used toimplement subband prediction directly in the subband domain without theneed for determining and applying a predictor (e.g. a long term delay)in the time domain.

The use of cross-subband terms in the subband predictors enablessignificantly improved frequency domain noise shaping propertiescompared to in-band predictors (which solely rely on in-bandprediction). By doing this, aliasing artifacts can be reduced, therebyenabling the use of subband prediction for relatively low bit rate audiocoding systems.

According to an aspect, a method for estimating a first sample of afirst subband of an audio signal is described. The first subband of theaudio signal may have been determined using an analysis filterbankcomprising a plurality of analysis filters which provide a plurality ofsubband signals in a plurality of subbands, respectively, from the audiosignal. The time domain audio signal may be submitted to an analysisfilterbank, thereby yielding a plurality of subband signals in aplurality of subbands. Each of the plurality of subbands typicallycovers a different frequency range of the audio signal, therebyproviding access to different frequency components of the audio signal.The plurality of subbands may have an equal or a uniform subbandspacing. The first subband corresponds to one of the plurality ofsubbands provided by the analysis filterbank.

The analysis filterbank may have various properties. A synthesisfilterbank comprising a plurality of synthesis filters may have similaror the same properties. The properties described for the analysisfilterbank and the analysis filters are also applicable to theproperties of the synthesis filterbank and the synthesis filters.Typically, the combination of analysis filterbank and synthesisfilterbank allow for a perfect reconstruction of the audio signal. Theanalysis filters of the analysis filterbank may be shift-invariant withrespect to one another. Alternatively or in addition, the analysisfilters of the analysis filterbank may comprise a common windowfunction. In particular, the analysis filters of the analysis filterbankmay comprise differently modulated versions of the common windowfunction. In an embodiment, the common window function is modulatedusing a cosine function, thereby yielding a cosine modulated analysisfilterbank. In particular, the analysis filterbank may comprise (or maycorrespond to) one or more of: an MDCT, a QMF, and/or an ELT transform.The common window function may have a finite duration K. The duration ofthe common window function may be such that succeeding samples of asubband signal are determined using overlapping segments of the timedomain audio signal. As such, the analysis filterbank may comprise anoverlapped transform. The analysis filters of the analysis filterbankmay form an orthogonal and/or an orthonormal basis. As a furtherproperty, the analysis filterbank may correspond to a critically sampledfilterbank. In particular, the number of samples of the plurality ofsubband signals may correspond to the number of samples of the timedomain audio signal.

The method may comprise determining a model parameter of a signal model.It should be noted that the signal model may be described using aplurality of model parameters. As such, the method may comprisedetermining the plurality of model parameters of the signal model. Themodel parameter(s) may be extracted from a received bitstream whichcomprises or which is indicative of the model parameter and of aprediction error signal. Alternatively, the model parameter(s) may bedetermined by fitting the signal model to the audio signal (e.g. on aframe by frame basis), e.g. using a means square error approach. Thesignal model may comprise one or more sinusoidal model components. Insuch a case, the model parameter may be indicative of the one or morefrequencies of the one or more sinusoidal model components. By way ofexample, the model parameter may be indicative of a fundamentalfrequency Ω of a multi-sinusoidal signal model, wherein themulti-sinusoidal signal comprises sinusoidal model components atfrequencies which correspond to multiples qΩ of the fundamentalfrequency Ω. As such, the multi-sinusoidal signal model may comprise aperiodic signal component, wherein the periodic signal componentcomprises a plurality of sinusoidal components and wherein the pluralityof sinusoidal components have a frequency which is a multiple of thefundamental frequency Ω. As will be shown in the present document, sucha periodic signal component may be used to model a delay in the timedomain (as used e.g. for long-term predictors). The signal model maycomprise one or more model parameters which are indicative of a shiftand/or a deviation of the signal model from a periodic signal model. Theshift and/or deviation may be indicative of a deviation of thefrequencies of the plurality of sinusoidal components of the periodicsignal model from respective multiples qΩ of the fundamental frequencyΩ.

The signal model may comprise a plurality of periodic signal components.Each of the periodic signal components may be described using one ormore model parameters. The model parameters may be indicative of aplurality of fundamental frequencies Ω₀, Ω₁, . . . , Ω_(M-1) of theplurality of periodic signal components. Alternatively or in addition,the signal model may be described by a pre-determined and/or anadjustable relaxation parameter (which may be one of the modelparameters). The relaxation parameter may be configured to even out orto smoothen the line spectrum of a periodic signal component. Specificexamples of signal models and associated model parameters are describedin the embodiment section of the present document.

The model parameter(s) may be determined such that a mean value of asquared prediction error signal is reduced (e.g. minimized). Theprediction error signal may be determined based on the differencebetween the first sample and the estimate of the first sample. Inparticular, the mean value of the squared prediction error signal may bedetermined based on a plurality of succeeding first samples of the firstsubband signal and based on a corresponding plurality of estimated firstsamples. In particular, it is proposed in the present document, to modelthe audio signal or at least the first subband signal of the audiosignal using a signal model which is described by one or more modelparameters. The model parameters are used to determine the one or moreprediction coefficients of a linear predictor which determines a firstestimated subband signal. The difference between the first subbandsignal and the first estimated subband signal yields a prediction errorsubband signal. The one or more model parameters may be determined suchthat the mean value of the squared prediction error subband signal isreduced (e.g. minimized).

The method may further comprise determining a prediction coefficient tobe applied to a previous sample of a first decoded subband signalderived from the first subband signal. In particular, the previoussample may be determined by adding a (quantized version) of theprediction error signal to a corresponding sample of the first subbandsignal. The first decoded subband signal may be identical to the firstsubband signal (e.g. in case of a lossless encoder). A time slot of theprevious sample is typically prior to a time slot of the first sample.In particular, the method may comprise determining one or moreprediction coefficients of a recursive (finite impulse response)prediction filter which is configured to determine the first sample ofthe first subband signal from one or more previous samples.

The one or more prediction coefficients may be determined based on thesignal model, based on the model parameter and based on the analysisfilterbank. In particular, a prediction coefficient may be determinedbased on an analytical evaluation of the signal model and of theanalysis filterbank. The analytical evaluation of the signal model andof the analysis filterbank may lead to the determination of a look-uptable and/or of an analytical function. As such, the predictioncoefficient may be determined using the look-up table and/or theanalytical function, wherein the look-up table and/or the analyticalfunction may be pre-determined based on the signal model and based onthe analysis filterbank. The look-up table and/or the analyticalfunction may provide the prediction coefficient(s) as a function of aparameter derived from the model parameter(s). The parameter derivedfrom the model parameter may e.g. be the model parameter or may beobtained from the model parameter using a pre-determined function. Assuch, the one or more prediction coefficients may be determined in acomputationally efficient manner using a pre-determined look-up tableand/or analytical function which provide the one or more predictioncoefficients in dependence (only) of the one or more parameters derived(only) from the one or more model parameters. Hence, the determinationof a prediction coefficient may be reduced to the simple look up of anentry within a look-up table.

As indicated above, the analysis filterbank may comprise or may exhibita modulated structure. As a result of such a modulated structure, it isobserved that the absolute value of the one or more predictioncoefficients is independent of an index number of the first subband.This means that the look-up table and/or the analytical function may beshift-invariant (apart from a sign value) with regards to the indexnumber of the plurality of subbands. In such cases, the parameterderived from the model parameter, i.e. the parameter which is entered tothe look-up table and/or to the analytical function in order todetermine the prediction coefficient may be derived by expressing themodel parameter in a relative manner with respect to a subband of theplurality of subbands.

As outlined above, the model parameter may be indicative of afundamental frequency Ω of a multi-sinusoidal signal model (e.g. of aperiodic signal model). In such cases, determining the predictioncoefficient may comprise determining a multiple of the fundamentalfrequency Ω which lies within the first subband. If a multiple of thefundamental frequency Ω lies within the first subband, a relative offsetof the multiple of the fundamental frequency Ω from a center frequencyof the first subband may be determined. In particular, the relativeoffset of the multiple of the fundamental frequency Ω which is closestto the center frequency of the first subband may be determined. Thelook-up table and/or the analytical function may be pre-determined suchthat the look-up table and/or the analytical function provide theprediction coefficient as a function of possible relative offsets from acenter frequency of a subband (e.g. as a function of a normalizedfrequency f and/or as a function of a shift parameter Θ, as described inthe present document). As such, the prediction coefficient may bedetermined based on the look-up table and/or based on the analyticalfunction using the determined relative offset. A pre-determined look-uptable may comprise a limited number of entries for a limited number ofpossible relative offsets. In such a case, the determined relativeoffset may be rounded to the nearest possible relative offset from thelimited number of possible relative offsets, prior to looking up theprediction coefficient from the look-up table.

On the other hand, if no multiple of the fundamental frequency Ω lieswithin the first subband, or rather, within an extended frequency rangesurrounding of the first subband, the prediction coefficient may be setto zero. In such cases, the estimate of the first sample may also bezero.

Determining the prediction coefficient may comprise selecting one of aplurality of look-up tables based on the model parameter. By way ofexample, the model parameter may be indicative of a fundamentalfrequency Ω of a periodic signal model. The fundamental frequency Ω of aperiodic signal model corresponds to a periodicity T of the periodicsignal model. It is shown in the present document that in case ofrelatively small periodicities T, a periodic signal model convergestowards a single-sinusoidal model. Furthermore, it is shown in thepresent document that in case of relatively large periodicities T, thelook-up tables are slowly varying with the absolute value of T andmainly depend on the relative offset (i.e. on the shift parameter Θ). Assuch, a plurality of look-up tables may be pre-determined for aplurality of different values of the periodicity T. The model parameter(i.e. the periodicity T) may be used to select an appropriate one of theplurality of look-up tables and the prediction coefficient may bedetermined based on the selected one of the plurality of look-up tables(using the relative offset, e.g. using the shift parameter Θ). As such,a model parameter (representing e.g. the periodicity T) which may have arelatively high precision may be decoded into a pair of parameters (e.g.the periodicity T and the relative offset) at a reduced precision. Thefirst parameter (e.g. the periodicity T) of the pair of parameters maybe used to select a particular look-up table and the second parameter(e.g. the relative offset) may be used to identify an entry within theselected look-up table.

The method may further comprise determining an estimate of the firstsample by applying the prediction coefficient to the previous sample.Applying the prediction coefficient to the previous sample may comprisemultiplying the prediction coefficient with the value of the previoussample, thereby yielding the estimate of the first sample. Typically, aplurality of first samples of the first subband signal is determined byapplying the prediction coefficient to a sequence of previous samples.Determining an estimate of the first sample may further compriseapplying a scaling gain to the prediction coefficient and/or to thefirst sample. The scaling gain (or an indication thereof may be usede.g. for long term prediction (LTP). In other words, the scaling gainmay result from a different predictor (e.g. from a long term predictor).The scaling gain may be different for different subbands. Furthermore,the scaling gain may be transmitted as part of the encoded audio signal.

As such, an efficient description of a subband predictor (comprising oneor more prediction coefficients) is provided by using a signal modelwhich is described by a model parameter. The model parameter is used todetermine the one or more prediction coefficients of the subbandpredictor. This means that an audio encoder does not need to transmit anindication of the one or more prediction coefficients, but an indicationof the model parameter. Typically, the model parameter can be encodedmore efficiently (i.e. with a lower number of bits) than the one or moreprediction coefficients. Hence, the use of model based predictionenables low bit rate subband encoding.

The method may further comprise determining a prediction mask indicativeof a plurality of previous samples in a plurality of prediction masksupport subbands. The plurality of prediction mask support subbands maycomprise at least one of the plurality of subbands, which is differentfrom the first subband. As such, the subband predictor may be configuredto estimate a sample of the first subband signal from samples of one ormore other subband signals from the plurality of subband signals, whichare different from the first subband signal. This is referred to in thepresent document as cross-subband prediction. The prediction mask maydefine the arrangement of the plurality of previous samples (e.g. a timelag with respect to the time slot of the first sample and/or a subbandindex lag with respect to the index number of the first subband) whichare used to estimate the first sample of the first subband signal.

The method may proceed in determining a plurality of predictioncoefficients to be applied to the plurality of previous samples. Theplurality of prediction coefficients may be determined based on thesignal model, based on the model parameter and based on the analysisfilterbank (e.g. using the model based prediction schemes outlined aboveand in the present document). As such, the plurality of predictioncoefficients may be determined using one or more model parameters. Inother words, a limited number of model parameters may be sufficient todetermine the plurality of prediction coefficients. This means that byusing model based subband prediction, cross-subband prediction may beimplemented in a bit-rate efficient manner.

The method may comprise determining an estimate of the first sample byapplying the plurality of prediction coefficients to the plurality ofprevious samples, respectively. Determining an estimate of the firstsample typically comprises determining the sum of the plurality ofprevious samples weighted by the plurality of respective predictioncoefficients.

As outlined above, the model parameter may be indicative of aperiodicity T. The plurality of look-up tables, which is used todetermine the one or more prediction coefficients, may comprise look-uptables for different values of periodicity T. In particular, theplurality of look-up tables may comprise look-up tables for differentvalues of periodicity T within the range of [T_(min), T_(max)] at apre-determined step size ΔT. As will be outlined in the presentdocument, T_(min), may be in the range of 0.25 and T_(max), may be inthe range of 2.5. T_(min), may be selected such that for T<T_(min), theaudio signal can be modeled using a signal model comprising a singlesinusoidal model component. T_(max), may be selected such that forT>T_(max), the look-up tables for the periodicities T_(max), toT_(max)+1 substantially correspond to the look-up tables for theperiodicities T_(max)−1 to T_(max). The same applies typically for theperiodicities T_(max)+n to T_(max)+n+1, for n≧0 in general.

The method may comprise determining the selected look-up table as thelook-up table for the periodicity T indicated by the model parameter.After having selected the look-up table comprising or indicating the oneor more prediction coefficients, a look-up parameter may be used toidentify the appropriate one or more entries within the selected look-uptable, which indicate the one or more prediction coefficients,respectively. The look-up parameter may correspond to or may be derivedfrom the shift parameter Θ.

The method may comprise, for a model parameter indicative of aperiodicity T>T_(max), determining a residual periodicity T_(r) bysubtracting an integer value from T, such that the residual periodicityT_(r) lies in the range [T_(max)−1, T_(max)]. The look-up table fordetermining the prediction coefficient may then be determined as thelook-up table for the residual periodicity T_(r).

The method may comprise, for a model parameter indicative of aperiodicity T<T_(min), selecting the look-up table for determining theone or more prediction coefficients as the look-up table for theperiodicity T_(min). Furthermore, the look-up parameter (e.g. the shiftparameter Θ) for identifying the one or more entries of the selectedlook-up table which provide the one or more prediction coefficients, maybe scaled in accordance to the ratio T_(min)/T. The one or moreprediction coefficients may then be determined using the selectedlook-up table and the scaled look-up parameter. In particular, the oneor more prediction coefficients may be determined based on the one ormore entries of the selected look-up table corresponding to the scaledlook-up parameter.

As such, the number of look-up tables may be limited to a pre-determinedrange [T_(min), T_(max)], thereby limiting the memory requirements of anaudio encoder/decoder. Nevertheless, the prediction coefficients may bedetermined for all possible values of the periodicity T using thepre-determined look-up tables, thereby enabling a computationallyefficient implementation of an audio encoder/decoder.

According to a further aspect, a method for estimating a first sample ofa first subband signal of an audio signal is described. As outlinedabove, the first subband signal of the audio signal may be determinedusing an analysis filterbank comprising a plurality of analysis filterswhich provide a plurality of subband signals in a plurality of subbands,respectively, from the audio signal. The features described above arealso applicable to the method described below.

The method comprises determining a prediction mask indicative of aplurality of previous samples in a plurality of prediction mask supportsubbands. The plurality of prediction mask support subbands comprises atleast one of the plurality of subbands, which is different from thefirst subband. In particular, the plurality of prediction mask supportsubbands may comprise the first subband and/or the plurality ofprediction mask support subbands may comprise one or more of theplurality of subbands directly adjacent to the first subband.

The method may further comprise determining a plurality of predictioncoefficients to be applied to the plurality of previous samples. Theplurality of previous samples is typically derived from the plurality ofsubband signals of the audio signal. In particular, the plurality ofprevious samples typically corresponds to the samples of a plurality ofdecoded subband signals. The plurality of prediction coefficients maycorrespond to the prediction coefficients of a recursive (finite impulseresponse) prediction filter which also takes into account one or moresamples of subbands which are different from the first subband. Anestimate of the first sample may be determined by applying the pluralityof prediction coefficients to the plurality of previous samples,respectively. As such, the method enables subband prediction using oneor more samples from other (e.g. adjacent) subbands. By doing this,aliasing artifacts caused by subband prediction based coders may bereduced.

The method may further comprise determining a model parameter of asignal model. The plurality of prediction coefficients may be determinedbased on the signal model, based on the model parameter and based on theanalysis filterbank. As such, the plurality of prediction coefficientsmay be determined using model-based prediction as described in thepresent document. In particular, the plurality of predictioncoefficients may be determined using a look-up table and/or ananalytical function. The look-up table and/or the analytical functionmay be pre-determined based on the signal model and based on theanalysis filterbank. Furthermore, the look-up table and/or theanalytical function may provide the plurality of prediction coefficients(only) as a function of a parameter derived from the model parameter.Hence, the model parameter may directly provide the plurality ofprediction coefficients using the look-up table and/or the analyticalfunction. As such, the model parameter may be used to efficientlydescribe the coefficient of a cross-subband predictor.

According to a further aspect, a method for encoding an audio signal isdescribed. The method may comprise determining a plurality of subbandsignals from the audio signal using an analysis filterbank comprising aplurality of analysis filters. The method may proceed in estimatingsamples of the plurality of subband signals using any one of theprediction methods described in the present document, thereby yielding aplurality of estimated subband signals. Furthermore, samples of aplurality of prediction error subband signals may be determined based oncorresponding samples of the plurality of subband signals and samples ofthe plurality of estimated subband signals. The method may proceed inquantizing the plurality of prediction error subband signals, and ingenerating an encoded audio signal. The encoded audio signal may beindicative of (e.g. may comprise) the plurality of quantized predictionerror subband signals. Furthermore, the encoded signal may be indicativeof (e.g. may comprise) one or more parameters used for estimating thesamples of the plurality of estimated subband signals, e.g. indicativeof one or more model parameters used for determining one or moreprediction coefficients which are then used for estimating the samplesof the plurality of estimated subband signals.

According to another aspect, a method for decoding an encoded audiosignal is described. The encoded audio signal is typically indicative ofa plurality of quantized prediction error subband signals and of one ormore parameters to be used for estimating samples of a plurality ofestimated subband signals. The method may comprise de-quantizing theplurality of quantized prediction error subband signals, therebyyielding a plurality of de-quantized prediction error subband signals.Furthermore, the method may comprise estimating samples of the pluralityof estimated subband signals using any of the prediction methodsdescribed in the present document. Samples of a plurality of decodedsubband signals may be determined based on corresponding samples of theplurality of estimated subband signals and based on samples of theplurality of de-quantized prediction error subband signals. A decodedaudio signal may be determined from the plurality of decoded subbandsignals using a synthesis filterbank comprising a plurality of synthesisfilters.

According to a further aspect, a system configured to estimate one ormore first samples of a first subband signal of an audio signal isdescribed. The first subband signal of the audio signal may bedetermined using an analysis filterbank comprising a plurality ofanalysis filters which provide a plurality of subband signals from theaudio signal in a plurality of respective subbands. The system maycomprise a predictor calculator configured to determine a modelparameter of a signal model. Furthermore, the predictor calculator maybe configured to determine one or more prediction coefficients to beapplied to one or more previous samples of a first decoded subbandsignal derived from the first subband signal. As such, the predictorcalculator may be configured to determine one or more predictioncoefficients of a recursive prediction filter, notably of a recursivesubband prediction filter. The one or more prediction coefficients maybe determined based on the signal model, based on the model parameterand based on the analysis filterbank (e.g. using the model-basedprediction methods described in the present document). Time slots of theone or more previous samples are typically prior to time slots of theone or more first samples. The system may further comprise a subbandpredictor configured to determine an estimate of the one or more firstsamples by applying the one or more prediction coefficients to the oneor more previous samples. According to another aspect, a systemconfigured to estimate one or more first samples of a first subbandsignal of an audio signal is described. The first subband signalcorresponds to a first subband of a plurality of subbands. The firstsubband signal is typically determined using an analysis filterbankcomprising a plurality of analysis filters which provide a plurality ofsubband signals for the plurality of subbands, respectively. The systemcomprises a predictor calculator configured to determine a predictionmask indicative of a plurality of previous samples in a plurality ofprediction mask support subbands. The plurality of prediction masksupport subbands comprises at least one of the plurality of subbands,which is different from the first subband. The predictor calculator isfurther configured to determine a plurality of prediction coefficients(or a recursive prediction filter) to be applied to the plurality ofprevious samples.

Furthermore, the system comprises a subband predictor configured todetermine an estimate of the one or more first samples by applying theplurality of prediction coefficients to the plurality of previoussamples, respectively.

According to another aspect, an audio encoder configured to encode anaudio signal is described. The audio encoder comprises an analysisfilterbank configured to determine a plurality of subband signals fromthe audio signal using a plurality of analysis filters. Furthermore, theaudio encoder comprises a predictor calculator and a subband predictoras described in the present document, which are configured to estimatesamples of the plurality of subband signals, thereby yielding aplurality of estimated subband signals. In addition, the encoder maycomprise a difference unit configured to determine samples of aplurality of prediction error subband signals based on correspondingsamples of the plurality of subband signals and of the plurality ofestimated subband signals. A quantizing unit may be used to quantize theplurality of prediction error subband signals. Furthermore, a bitstreamgeneration unit may be configured to generate an encoded audio signalindicative of the plurality of quantized prediction error subbandsignals and of one or more parameters (e.g. one or more modelparameters) used for estimating the samples of the plurality ofestimated subband signals.

According to a further aspect, an audio decoder configured to decode anencoded audio signal is described. The encoded audio signal isindicative of (e.g. comprises) the plurality of quantized predictionerror subband signals and one or more parameters used for estimatingsamples of a plurality of estimated subband signals. The audio decodermay comprise an inverse quantizer configured to de-quantizing theplurality of quantized prediction error subband signals, therebyyielding a plurality of de-quantized prediction error subband signals.Furthermore, the decoder comprises a predictor calculator and a subbandpredictor as described in the present document, which are configured toestimate samples of the plurality of estimated subband signals. Asumming unit may be used to determine samples of a plurality of decodedsubband signals based on corresponding samples of the plurality ofestimated subband signals and based on samples of the plurality ofde-quantized prediction error subband signals. Furthermore, a synthesisfilterbank may be used to determine a decoded audio signal from theplurality of decoded subband signals using a plurality of synthesisfilters.

According to a further aspect, a software program is described. Thesoftware program may be adapted for execution on a processor and forperforming the method steps outlined in the present document whencarried out on the processor.

According to another aspect, a storage medium is described. The storagemedium may comprise a software program adapted for execution on aprocessor and for performing the method steps outlined in the presentdocument when carried out on the processor.

According to a further aspect, a computer program product is described.The computer program may comprise executable instructions for performingthe method steps outlined in the present document when executed on acomputer.

It should be noted that the methods and systems including its preferredembodiments as outlined in the present patent application may be usedstand-alone or in combination with the other methods and systemsdisclosed in this document. Furthermore, all aspects of the methods andsystems outlined in the present patent application may be arbitrarilycombined. In particular, the features of the claims may be combined withone another in an arbitrary manner.

SHORT DESCRIPTION OF THE FIGURES

The present invention is described below by way of illustrativeexamples, not limiting the scope or spirit of the invention, withreference to the accompanying drawings, in which:

FIG. 1 depicts the block diagram of an example audio decoder applyinglinear prediction in a filterbank domain (i.e. in a subband domain);

FIG. 2 shows example prediction masks in a time frequency grid;

FIG. 3 illustrates example tabulated data for a sinusoidal model basedpredictor calculator;

FIG. 4 illustrates example noise shaping resulting from in-band subbandprediction;

FIG. 5 illustrates example noise shaping resulting from cross-bandsubband prediction; and

FIG. 6a depicts an example two-dimensional quantization grid underlyingthe tabulated data for a periodic model based predictor calculation;

FIG. 6b illustrates the use of different prediction masks for differentranges of signal periodicities; and

FIGS. 7a and 7b show flow charts of example encoding and decodingmethods using model based subband prediction.

DETAILED DESCRIPTION

The below-described embodiments are merely illustrative for theprinciples of the present invention for model based prediction in acritically sampled filterbank. It is understood that modifications andvariations of the arrangements and the details described herein will beapparent to others skilled in the art. It is the intent, therefore, tobe limited only by the scope of the impending patent claims and not bythe specific details presented by way of description and explanation ofthe embodiments herein.

FIG. 1 depicts the block diagram of an example audio decoder 100applying linear prediction in a filterbank domain (also referred to assubband domain). The audio decoder 100 receives a bit stream comprisinginformation regarding a prediction error signal (also referred to as theresidual signal) and possibly information regarding a description of apredictor used by a corresponding encoder to determine the predictionerror signal from an original input audio signal. The informationregarding the prediction error signal may relate to subbands of theinput audio signal and the information regarding a description of thepredictor may relate to one or more subband predictors.

Given the received bit stream information, the inverse quantizer 101 mayoutput samples 111 of the prediction error subband signals. Thesesamples may be added to the output 112 of the subband predictor 103 andthe sum 113 may be passed to a subband buffer 104 which keeps a recordof previously decoded samples 113 of the subbands of the decoded audiosignal. The output of the subband predictor 103 may be referred to asthe estimated subband signals 112. The decoded samples 113 of thesubbands of the decoded audio signal may be submitted to a synthesisfilterbank 102 which converts the subband samples to the time domain,thereby yielding time domain samples 114 of the decoded audio signal.

In other words, the decoder 100 may operate in the subband domain. Inparticular, the decoder 100 may determine a plurality of estimatedsubband signals 112 using the subband predictor 103. Furthermore, thedecoder 100 may determine a plurality of residual subband signals 111using the inverse quantizer 101. Respective pairs of the plurality ofestimated subband signals 112 and the plurality of residual subbandsignals 111 may be added to yield a corresponding plurality of decodedsubband signals 113. The plurality of decoded subband signals 113 may besubmitted to a synthesis filterbank 102 to yield the time domain decodedaudio signal 114.

In an embodiment of the subband predictor 103, a given sample of a givenestimated subband signal 112 may be obtained by a linear combination ofsubband samples in the buffer 104 which corresponds to a different timeand to a different frequency (i.e. different subband) than the givensample of the given estimated subband signal 112. In other words, asample of an estimated subband signal 112 at a first time instant and ina first subband may be determined based on one or more samples of thedecoded subband signals 113 which relate to a second time instant(different from the first time instant) and which relate to a secondsubband (different from the first subband). The collection of predictioncoefficients and their attachment to a time and frequency mask maydefine the predictor 103, and this information may be furnished by thepredictor calculator 105 of the decoder 100. The predictor calculator105 outputs the information defining the predictor 103 by means of aconversion of signal model data included in the received bit stream. Anadditional gain may be transmitted which modifies the scaling of theoutput of the predictor 103. In an embodiment of the predictorcalculator 105, the signal model data is provided in the form of anefficiently parametrized line spectrum, wherein each line in theparametrized line spectrum, or a group of subsequent lines of theparametrized line spectrum, is used to point to tabulated values ofpredictor coefficients. As such, the signal model data provided withinthe received bit stream may be used to identify entries within apre-determined look-up table, wherein the entries from the look-up tableprovide one or more values for the predictor coefficients (also referredto as the prediction coefficients) to be used by the predictor 103. Themethod applied for the table look-up may depend on the trade-offsbetween complexity and memory requirements. For instance, a nearestneighbor type look-up may be used to achieve the lowest complexity,whereas an interpolating look-up method may provide similar performancewith a smaller table size.

As indicated above, the received bit stream may comprise one or moreexplicitly transmitted gains (or explicitly transmitted indications ofgains). The gains may be applied as part of or after the predictoroperation. The one or more explicitly transmitted gains may be differentfor different subbands. The explicitly transmitted (indications of)additional gains are provided in addition to one or more modelparameters which are used to determined the prediction coefficients ofthe predictor 103. As such, the additional gains may be used to scalethe prediction coefficients of the predictor 103.

FIG. 2 shows example prediction mask supports in a time frequency grid.The prediction mask supports may be used for predictors 103 operating ina filterbank with a uniform time frequency resolution such as a cosinemodulated filterbank (e.g. an MDCT filterbank). The notation isillustrated by diagram 201, in that a target darkly shaded subbandsample 211 is the output of a prediction based on a lightly shadedsubband sample 212. In the diagrams 202-205, the collection of lightlyshaded subband samples indicates the predictor mask support. Thecombination of source subband samples 212 and target subband samples 211will be referred to as a prediction mask 201. A time-frequency grid maybe used to arrange subband samples in the vicinity of the target subbandsample. The time slot index is increasing from left to right and thesubband frequency index is increasing from bottom to top. FIG. 2 showsexample cases of prediction masks and predictor mask supports and itshould be noted that various other prediction masks and predictor masksupports may be used. The example prediction masks are:

-   -   Prediction mask 202 defines in-band prediction of an estimated        subband sample 221 at time instant k from two previous decoded        subband samples 222 at time instants k−1 and k−2.    -   Prediction mask 203 defines cross-band prediction of an        estimated subband sample 231 at time instant k and in subband n        based on three previous decoded subband samples 232 at time        instant k−1 and in subbands n−1, n, n+1.    -   Prediction mask 204 defines cross-band prediction of three        estimated subband samples 241 at time instant k and in three        different subbands n−1, n, n+1 based on three previous decoded        subband samples 242 at time instant k−1 and in subbands n−1, n,        n+1. The cross-band prediction may be performed such that each        estimated subband sample 241 may be determined based on all of        the three previous decoded subband samples 242 in the subbands        n−1, n, n+1.    -   Prediction mask 205 defines cross-band prediction of an        estimated subband sample 251 at time instant k and in subband n        based on twelve previous decoded subband samples 252 at time        instants k−2, k−3, k−4, k−5 and in subbands n−1, n, n+1.

FIG. 3 illustrates tabulated data for a sinusoidal model based predictorcalculator 105 operating in a cosine modulated filterbank. Theprediction mask support is that of diagram 204. For a given frequencyparameter, the subband with the nearest subband center frequency may beselected as central target subband. The difference between the frequencyparameter and the center frequency of the central target subband may becomputed in units of the frequency spacing of the filterbank (bins).This gives a value between −0.5 and 0.5 which may be rounded to thenearest available entry in the tabulated data, depicted by the abscissasof the nine graphs 301 of FIG. 3. This produces a 3×3 matrix ofcoefficients which is to be applied to the most recent values of theplurality of decoded subband signals 113 in the subband buffer 104 ofthe target subband and its two adjacent subbands. The resulting 3×1vector constitutes the contribution of the subband predictor 103 tothese three subbands for the given frequency parameter. The process maybe repeated in an additive fashion for all the sinusoidal components inthe signal model.

In other words, FIG. 3 illustrates an example of a model-baseddescription of a subband predictor. It is assumed that the input audiosignal comprises one or more sinusoidal components at fundamentalfrequencies Ω₀, Ω₁, . . . , Ω_(M-1). For each of the one or moresinusoidal components, a subband predictor using a pre-determinedprediction mask (e.g. the prediction mask 204) may be determined. Afundamental frequency Ω of the input audio signal may lie within one ofthe subbands of the filterbank. This subband may be referred to as thecentral subband for this particular fundamental frequency Ω. Thefundamental frequency Ω may be expressed as a value ranging from −0.5and 0.5 relative to the center frequency of the central subband. Anaudio encoder may transmit information regarding the fundamentalfrequency Ω to the decoder 100. The predictor calculator 105 of thedecoder 100 may use the three-by-three matrix of FIG. 3 to determine athree-by-three matrix of prediction coefficients by determining thecoefficient value 302 for the relative frequency value 303 of thefundamental frequency Ω. This means that the coefficient for a subbandpredictor 103 using a prediction mask 204 can be determined using onlythe received information regarding the particular fundamental frequencyΩ. In other words, by modeling an input audio signal using e.g. a modelof one of more sinusoidal components, a bit-rate efficient descriptionof a subband predictor can be provided.

FIG. 4 illustrates example noise shaping resulting from in-band subbandprediction in a cosine modulated filterbank. The signal model used forperforming in-band subband prediction is a second order autoregressivestochastic process with a peaky resonance, as described by a secondorder differential equation driven by random Gaussian white noise. Thecurve 401 shows the measured magnitude spectrum for a realization of theprocess. For this example, the prediction mask 202 of FIG. 2 is applied.That is, the predictor calculator 105 furnishes the subband predictor103 for a given target subband 221 based on previous subband samples 222in the same subband only. Replacing the inverse quantizer 101 by aGaussian white noise generator leads to a synthesized magnitude spectrum402. As can be seen, strong alias artifacts occur in the synthesis, asthe synthesized spectrum 402 comprises peaks which do not coincide withthe original spectrum 401.

FIG. 5 illustrates the example noise shaping resulting from cross-bandsubband prediction. The setting is the same as that of FIG. 4, exceptfor the fact that the prediction mask 203 is applied. Hence, calculator105 furnishes the predictor 103 for a given target subband 231 based onprevious subband samples 232 in the target subband and in its twoadjacent subbands. As it can be seen from FIG. 5, the spectrum 502 ofthe synthesized signal substantially coincides with the spectrum 501 ofthe original signal, i.e. the alias problems are substantiallysuppressed when using cross-band subband prediction.

As such, FIGS. 4 and 5 illustrate that when using cross-band subbandprediction, i.e. when predicting a subband sample based on previoussubband samples of one or more adjacent subbands, aliasing artifactscaused by subband prediction can be reduced. As a result, subbandprediction may also be applied in the context of low bit rate audioencoders without the risk of causing audible aliasing artifacts. The useof cross-band subband prediction typically increases the number ofprediction coefficients. However, as shown in the context of FIG. 3, theuse of models for the input audio signal (e.g. the use of a sinusoidalmodel or a periodic model) allows for an efficient description of thesubband predictor, thereby enabling the use of cross-band subbandprediction for low bit rate audio coders.

In the following, a description of the principles of model basedprediction in a critically sampled filterbank will be outlined withreference to FIGS. 1-6, and by adding appropriate mathematicalterminology.

A possible signal model underlying linear prediction is that of azero-mean weakly stationary stochastic process x(t) whose statistics isdetermined by its autocorrelation function r(τ)=E{x(t)x(t−τ)}. As a goodmodel for the critically sampled filterbanks to be considered here, onelets {w_(α): α∈A} be a collection of real valued synthesis waveformsw_(α) (t) constituting an orthonormal basis. In other words, thefilterbank may be represented by the waveforms {w_(α): α∈A}. Subbandsamples of a time domain signal s(t) are obtained by inner products

$\begin{matrix}{{{\left\langle {s,w_{\alpha}} \right\rangle = {\int_{- \infty}^{\infty}{{s(t)}{w_{\alpha}(t)}{dt}}}},}\ } & (1)\end{matrix}$and the signal is recovered by

$\begin{matrix}{{{s(t)} = {\sum\limits_{\alpha \in A}^{\;}{\left\langle {s,w_{\alpha}} \right\rangle{w_{\alpha}(t)}}}},} & (2)\end{matrix}$

The subband samples

x, w_(α)

of the process x(t) are random variables, whose covariance matrix R_(αβ)is determined by the autocorrelation function r(τ) as followsR _(αβ) =E{

x,w _(α)

x,w _(β)

}=

W _(αβ) r

,  (3)where W_(αβ) (τ) is the cross correlation of two synthesis waveforms

$\begin{matrix}{{{W_{\alpha\beta}(\tau)} = {\int_{- \infty}^{\infty}{{w_{\alpha}(t)}{w_{\beta}\left( {t - \tau} \right)}{{dt}.}}}}\ } & (4)\end{matrix}$

A linear prediction of the subband sample (x, w_(α)) from a collectionor decoded subband samples {

x, w_(β)

: β∈B} is defined by

$\begin{matrix}{\sum\limits_{\beta \in B}\;{c_{\beta}{\left\langle {x,w_{\beta}} \right\rangle.}}} & (5)\end{matrix}$

In equation (5), the set B defines the source subband samples, i.e. theset B defines the prediction mask support. The mean value of the squaredprediction error is given by

$\begin{matrix}{{{E\left\{ \left( {{\sum\limits_{\beta \in B}\;{c_{\beta}\left\langle {x,w_{\beta}} \right\rangle}} - \left\langle {x,w_{\alpha}} \right\rangle} \right)^{2} \right\}} = {{\sum\limits_{\beta,{\gamma \in B}}\;{c_{\gamma}R_{\gamma\;\beta}c_{\beta}}} - {2{\sum\limits_{\beta \in B}\;{R_{\alpha\;\beta}c_{\beta}}}} + R_{\alpha\alpha}}},} & (6)\end{matrix}$and the least mean square error (MSE) solution is obtained by solvingthe normal equations for the prediction coefficients c_(β),

$\begin{matrix}{{{\sum\limits_{\beta \in B}\;{R_{\gamma\;\beta}c_{\beta}}} = R_{\gamma\;\alpha}},{\gamma \in {B.}}} & (7)\end{matrix}$

When the prediction coefficients satisfy equation (7), the right handside of equation (6) reduces to R_(αα)−Σ_(β)R_(αβ)c_(β). The normalequations (7) may be solved in an efficient manner using e.g. theLevinson-Durbin algorithm.

It is proposed in the present document to transmit a parametricrepresentation of a signal model from which the prediction coefficients{c_(β): β∈B} can be derived in the predictor calculator 105. Forexample, the signal model may provide a parametric representation of theautocorrelation function r(τ) of the signal model. The decoder 100 mayderive the autocorrelation function r(τ) using the received parametricrepresentation and may combine the autocorrelation function r(τ) withthe synthesis waveform cross correlation W_(αβ)(τ) in order to derivethe covariance matrix entries required for the normal equations (7).These equations may then be solved to obtain the predictioncoefficients.

In other words, a to-be-encoded input audio signal may be modeled by aprocess x(t) which can be described using a limited number of modelparameters. In particular, the modeling process x(t) may be such thatits autocorrelation function r(τ)=E{x(t)x(t−τ)} can be described using alimited number of parameters. The limited number of parameters fordescribing the autocorrelation function r(τ) may be transmitted to thedecoder 100. The predictor calculator 105 of the decoder 100 maydetermine the autocorrelation function r(τ) from the received parametersand may use equation (3) to determine the covariance matrix R_(αβ) ofthe subband signals from which the normal equation (7) can bedetermined. The normal equation (7) can then be solved by the predictorcalculator 105, thereby yielding the prediction coefficients c_(β).

In the following, example signal models are described which may be usedto apply the above described model based prediction scheme in anefficient manner. The signal models described in the following aretypically highly relevant for coding audio signals, e.g. for codingspeech signals.

An example of a signal model is given by the sinusoidal processx(t)=a cos(ξt)+b sin(ξt),  (8)where the random variables a, b are uncorrelated, have zero mean, andvariance one. The autocorrelation function of this sinusoidal process isgiven byr(τ)=cos(ξτ).  (9)

A generalization of such a sinusoidal process is a multi-sine modelcomprising a set of (angular) frequencies S, i.e. comprising a pluralityof different (angular) frequencies ξ,

$\begin{matrix}{{x(t)} = {{\sum\limits_{\xi \in S}\;{a_{\xi}{\cos\left( {\xi\; t} \right)}}} + {b_{\xi}{{\sin\left( {\xi\; t} \right)}.}}}} & (10)\end{matrix}$

Assuming that all the random variables a_(ξ), b_(ξ) are pairwiseuncorrelated, have zero mean, and variance one, the multi-sine processhas the autocorrelation function

$\begin{matrix}{{r(\tau)} = {\sum\limits_{\xi \in S}\;{{\cos\left( {\xi\; t} \right)}.}}} & (11)\end{matrix}$

The power spectral density (PSD) of the multi-sine process (whichcorresponds to the Fourier transform of the autocorrelation function),is the line spectrum

$\begin{matrix}{{P(\omega)} = {\frac{1}{2}{\sum\limits_{\xi \in S}{\left( {{\delta\left( {\omega - \xi} \right)} + {\delta\left( {\omega + \xi} \right)}} \right).}}}} & (12)\end{matrix}$

Numerical considerations can lead to the replacement of the puremulti-sine process with the autocorrelation function of equation processwith a relaxed multi-sine process having the autocorrelation function

${r(\tau)} = {{\exp\left( {{- ɛ}{\tau }} \right)}{\sum\limits_{\xi \in S}\;{\cos\left( {\xi\; t} \right)}}}$where ε>0 being a relatively small relaxation parameter. The lattermodel leads to a strictly positive PSD without impulse functions.

Examples of compact descriptions of the set S of frequencies of amulti-sine model are as follows

-   -   1. A single fundamental frequency Ω: S={Ων: ν=1, 2, . . . }    -   2. M fundamental frequencies: Ω₀, Ω₁, . . . , Ω_(M-1):        S={Ω_(k)ν: ν=1, 2, . . . , k=0, 1, . . . M−1}    -   3. A single side band shifted fundamental frequency Ω, θ:        S={Ω(ν+θ): ν=1, 2, . . . }    -   4. A slightly inharmonic model: Ωa: S={Ων·(1+aν²)^(1/2): ν=1, 2,        . . . }, with a describing the inharmonic component of the        model.

As such, a (possibly relaxed) multi-sine model exhibiting a PSD given byequation (12) may be described in an efficient manner using one of theexample descriptions listed above. By way of example, a complete set Sof frequencies of the line spectrum of equation (12) may be describedusing only a single fundamental frequency Ω. If the to-be-encoded inputaudio signal can be well described using a multi-sine model exhibiting asingle fundamental frequency Ω, the model based predictor may bedescribed by a single parameter (i.e. by the fundamental frequency Ω),regardless the number of prediction coefficients (i.e. regardless theprediction mask 202, 203, 204, 205) used by the subband predictor 103.

Case 1 for describing the set S of frequencies yields a process x(t)which models input audio signals with a period T=2π/Ω. Upon inclusion ofthe zero frequency (DC) contribution with variance ½ to equation (11)and subject to rescaling of the result by the factor 2/T, theautocorrelation function of the periodic model process x(t) may bewritten as

$\begin{matrix}{{r(\tau)} = {\sum\limits_{k \in Z}\;{{\delta\left( {\tau - {k\; T}} \right)}.}}} & (13)\end{matrix}$

With the definition of a relaxation factor ρ=exp((−Tε), theautocorrelation function of the relaxed version of the periodic model isgiven by

$\begin{matrix}{{r(\tau)} = {\sum\limits_{k \in Z}{\rho^{k}{{\delta\left( {\tau - {k\; T}} \right)}.}}}} & (14)\end{matrix}$

Equation (14) also corresponds to the autocorrelation function of aprocess defined by a single delay loop fed with white noise z(t), thatis, of the model processx(t)=ρx(t−T)+√{square root over (1−ρ²)}z(t).  (15)

This means that the periodic process which exhibits a single fundamentalfrequency Ω corresponds to a delay in the time domain, with the delaybeing T=2π/Ω.

The above mentioned global signal models typically have a flat largescale power spectrum, due to the unit variance assumption of thesinusoidal amplitude parameters a_(ξ), b_(ξ). It should be noted,however, that the signal models are typically only considered locallyfor a subset of subbands of a critically sampled filterbank, wherein thefilterbank is instrumental in the shaping of the overall spectrum. Inother words, for a signal that has a spectral shape with slow variationcompared to the subband widths, the flat power spectrum models willprovide a good match to the signal, and subsequently, the model basedpredictors will offer adequate levels of prediction gain.

More generally, the PSD model could be described in terms of standardparameterizations of autoregressive (AR) or autoregressive movingaverage (ARMA) processes. This would increase the performance ofmodel-based prediction at the possible expense of an increase indescriptive model parameters.

Another variation is obtained by abandoning the stationarity assumptionfor the stochastic signal model. The autocorrelation function thenbecomes a function of two variables r(t,s)=E{x(t)x(s)}. For instance,relevant non-stationary sinusoidal models may include amplitude (AM) andfrequency modulation (FM). Furthermore, a more deterministic signalmodel may be employed. As will be seen in some of the examples below,the prediction can have a vanishing error in some cases. In such cases,the probabilistic approach can be avoided. When the prediction isperfect for all signals in a model space, there is no need to perform amean value of prediction performance by means of a probability measureon the considered model space.

In the following, various aspects regarding modulated filterbanks aredescribed. In particular, aspects are described which have an influenceon the determination of the covariance matrix, thereby providingefficient means for determining the prediction coefficients of a subbandpredictor.

A modulated filterbank may be described as having a two-dimensionalindex set of synthesis waveforms α=(n,k) where n=0, 1, . . . is thesubband index (frequency band) and where k∈Z is the subband sample index(time slot). For ease of exposition, it is assumed that the synthesiswaveforms are given in continuous time and are normalized to a unit timestride,w _(n,k)(t)=u _(n)(t−k),  (16)where

$\begin{matrix}{{{u_{n}(t)} = {{v(t)}{\cos\left\lbrack {{\pi\left( {n + \frac{1}{2}} \right)}\left( {t + \frac{1}{2}} \right)} \right\rbrack}}},} & (17)\end{matrix}$in case of a cosine modulated filterbank. It is assumed that the windowfunction ν(t) is real valued and even. Up to minor variations of themodulation rule, this covers a range of highly relevant cases such asMDCT (Modified Discrete Cosine Transform), QMF (Quadrature MirrorFilter), and ELT (Extended Lapped Transforms) with L subbands uponsampling at a time step 1/L. The window is supposed to be of finiteduration or length with support included in the interval [−K/2, K/1],where K is the overlap factor of the overlapped transform and where Kindicates the length of the window function.

Due to the shift invariant structure, one finds that the crosscorrelation function of the synthesis waveform (as defined in equation(4)) can be written as

$\begin{matrix}{{W_{n,k,m,l}(\tau)} = {{\int_{- \infty}^{\infty}{{w_{n,k}(t)}{w_{m,l}\left( {t - \tau} \right)}\mspace{11mu}{dt}}} = {\int_{- \infty}^{\infty}{{u_{n}(t)}{u_{m}\left( {t - l + k - \tau} \right)}\mspace{11mu}{{dt}.}}}}} & (18)\end{matrix}$

That is, W_(n,k,m,l)(τ)=U_(n,m)(τ−l+k), with the definitionU_(n,m)(τ)=W_(n,0,m,0)(τ). The modulation structure (17) allows forfurther expansion into

$\begin{matrix}{{U_{n,m}(\tau)} = {{\frac{1}{2}{\kappa_{n - m}(\tau)}\cos{\frac{\pi}{2}\left\lbrack {{\left( {n + m + 1} \right)\tau} + \left( {n - m} \right)} \right\rbrack}} + {\frac{1}{2}{\kappa_{n + m + 1}(\tau)}\cos{{\frac{\pi}{2}\left\lbrack {{\left( {n - m} \right)\tau} + \left( {n + m + 1} \right)} \right\rbrack}.}}}} & (19)\end{matrix}$where the kernel function κ_(ν) represents a sampling with thefilterbank subband step in the frequency variable of the Wigner-Villedistribution of the filterbank window

$\begin{matrix}{{\kappa_{v}(\tau)} = {\int_{- \infty}^{\infty}{{v\left( {t + \frac{\tau}{2}} \right)}{v\left( {t - \frac{\tau}{2}} \right)}\cos\;\left( {\pi\; v\; t} \right)\mspace{11mu} d\;{t.}}}} & (20)\end{matrix}$

The kernel is real and even in both ν and τ, due to the above mentionedassumptions on the window function ν(t). Its Fourier transform is theproduct of shifted window responses,

$\begin{matrix}{{{\hat{\kappa}}_{v}(\omega)} = {{\hat{v}\left( {\omega + {\frac{\pi}{2}v}} \right)}{{\hat{v}\left( {\omega - {\frac{\pi}{2}v}} \right)}.}}} & (21)\end{matrix}$

It can be seen from equations (20) and (21) that the kernel κ_(ν)(τ)vanishes for |τ|>K and has a rapid decay as a function of |ν| fortypical choices of filterbank windows ν(t). As a consequence, the secondterm of equation (19) involving ν=n+m+1 can often be neglected exceptfor the lowest subbands.

For the autocorrelation function r(τ) of a given signal model, the abovementioned formulas can be inserted into the definition of the subbandsample covariance matrix given by equation (3). One getsR_(n,k,m,l)=R_(n,m)[k−l] with the definition

$\begin{matrix}{{R_{n,m}\lbrack\lambda\rbrack} = {\int_{- \infty}^{\infty}{{U_{n,m}(\tau)}{r\left( {\tau + \lambda} \right)}\mspace{11mu} d\;{\tau.}}}} & (22)\end{matrix}$

As a function of the power spectral density P(ω) of the given signalmodel (which corresponds to the Fourier transform of the autocorrelationfunction r(τ)), one finds that

$\begin{matrix}{{R_{n,m}\lbrack\lambda\rbrack} = {\frac{1}{2\pi}{\int_{- \infty}^{\infty}{{{\hat{U}}_{n,m}(\omega)}{P(\omega)}{\exp\left( {{- i}\;\omega\;\lambda} \right)}d\;{\omega.}}}}} & (23)\end{matrix}$where Û_(n,m)(ω) is the Fourier transform of U_(n,m)(τ), where n, midentify subband indexes, and where λ represents a time slot lag(λ=k−l). The expression of equation (23) may be rewritten as

$\begin{matrix}{{R_{n,m}\lbrack\lambda\rbrack} = {{\frac{1}{4\pi}{\int_{- \infty}^{\infty}{{{\hat{\kappa}}_{n - m}\left( {\omega - {\frac{\pi}{2}\left( {n + m + 1} \right)}} \right)}{P(\omega)}{\cos\left( {{\omega\;\lambda} - {\frac{\pi}{2}\left( {n - m} \right)}} \right)}d\;\omega}}} + {\frac{1}{4\pi}{\int_{- \infty}^{\infty}{{{\hat{\kappa}}_{n + m + 1}\left( {\omega - {\frac{\pi}{2}\left( {n - m} \right)}} \right)}{P(\omega)}{\cos\left( {{\omega\;\lambda} - {\frac{\pi}{2}\left( {n + m + 1} \right)}} \right)}d\;{\omega.}}}}}} & (24)\end{matrix}$

An important observation is that the first term of equation (24) hasessentially an invariance property with respect to frequency shifts. Ifthe second term of equation (24) is neglected and P(ω) is shifted by aninteger ν times the subband spacing π to P(ω−πν), one finds acorresponding shift in the covariances R_(n,m)[λ]=±R_(n-ν,m-ν)[λ], wherethe sign depends on the (integer) values of the time lag λ. Thisreflects the advantage of using a filterbank with a modulationstructure, as compared to the general filter bank case.

Equation (24) provides an efficient means for determining the matrixcoefficients of the subband sample covariance matrix when knowing thePSD of the underlying signal model. By way of example, in case of asinusoidal model based prediction scheme which makes use of a signalmodel x(t) comprising a single sinusoid at the (angular) frequency ξ,the PSD is given by

${P(\omega)} = {\frac{1}{2}{\left( {{\delta\left( {\omega - \xi} \right)} + {\delta\left( {\omega + \xi} \right)}} \right).}}$Inserting P(ω) into equation (24) gives four terms of which three can beneglected under the assumption that n+m+1 is large. The remaining termbecomes

$\begin{matrix}{{{R_{n,m}\lbrack\lambda\rbrack} \approx {\frac{1}{8\pi}{{\hat{\kappa}}_{n - m}\left( {\xi - {\frac{\pi}{2}\left( {n + m + 1} \right)}} \right)}{\cos\left( {{\xi\lambda} - {\frac{\pi}{2}\left( {n - m} \right)}} \right)}}} = {\frac{1}{8\pi}{\hat{v}\left( {\xi - {\pi\left( {n + \frac{1}{2}} \right)}} \right)}{\hat{v}\left( {\xi - {\pi\left( {m + \frac{1}{2}} \right)}} \right)}{{\cos\left( {{\xi\lambda} - {\frac{\pi}{2}\left( {n - m} \right)}} \right)}.}}} & (25)\end{matrix}$

Equation (25) provides an efficient means for determining the subbandcovariance matrix R_(n,m). A subband sample

x,w_(p,0)

can be reliably predicted by a collection of surrounding subband samples{

x,w_(n,k)

: (n,k)∈B} which are assumed to be influenced significantly by theconsidered frequency. The absolute frequency ξ can be expressed inrelative terms, relative to the center frequency

$\pi\left( {p + \frac{1}{2}} \right)$of a subband, as

${\xi = {\pi\left( {p + \frac{1}{2} + f} \right)}},$where p the subband index of the subband which comprises the frequencyξ, and where f is a normalized frequency parameter which takes on valuesbetween −0.5 and +0.5 and which indicates the position of the frequencyξ relative of the center frequency of the subband p. Having determinedthe subband covariance matrix R_(n,m), the predictor coefficientsc_(m)[l] which are applied to a subband sample in subband m at sampleindex l for estimating a subband sample in subband n at sample index kare found by solving the normal equations (7), which for the case athand can be written

$\begin{matrix}{{{\sum\limits_{{({m,l})} \in B}{{R_{n,m}\left\lbrack {k - l} \right\rbrack}{c_{m}\lbrack l\rbrack}}} = {R_{n,p}\lbrack k\rbrack}},{\left( {n,k} \right) \in {B.}}} & (26)\end{matrix}$

In equation (26), the set B describes the prediction mask support asillustrated e.g. in FIG. 2. In other words, the set B identifies thesubbands m and the sample indexes l which are used to predict a targetsample.

In the following, solutions of the normal equations (26) for differentprediction mask supports (as shown in FIG. 2) are provided in anexemplary manner. The example of a causal second order in-band predictoris obtained by selecting the prediction mask support B={(p, −1), (p−2)}.This prediction mask support corresponds to the prediction mask 202 ofFIG. 2. The normal equations (26) for this two tap prediction, using theapproximation of equation (25), become

$\begin{matrix}{{{{\hat{v}\left( {\xi - {\pi\left( {p + \frac{1}{2}} \right)}} \right)}^{2}{\sum\limits_{{I = {- 1}},{- 2}}{{\cos\left( {\xi\left( {k - l} \right)} \right)}{c_{p}\lbrack l\rbrack}}}} = {{\hat{v}\left( {\xi - {\pi\left( {p + \frac{1}{2}} \right)}} \right)}^{2}{\cos\left( {{- \xi}\; k} \right)}}},} & (27) \\{\mspace{20mu}{{k = {- 1}},{- 2.}}} & \;\end{matrix}$

A solution to equation (27) is given by c_(p)[−1]=2 cos(ξ), c_(p)[−2]=−1and it is unique as long the frequency

$\xi = {\pi\left( {p + \frac{1}{2} + f} \right)}$is not chosen such that {circumflex over (ν)}(f)=0. One finds that themean value of the squared prediction error according to equation (6)vanishes. Consequently, the sinusoidal prediction is perfect, up to theapproximation of equation (25). The invariance property to frequencyshifts is illustrated here by the fact that using the definition

${\xi = {\pi\left( {p + \frac{1}{2} + f} \right)}},$the prediction coefficient c_(p)[−1] can be rewritten in terms of thenormalized frequency f, as c_(p)[−1]=−2(−1)^(p) sin(πf). This means thatthe prediction coefficients are only dependent on the normalizedfrequency f within a particular subband. The absolute values of theprediction coefficients are, however, independent of the subband indexp.

As discussed above for FIG. 4, in-band prediction has certainshortcomings with respect to alias artifacts in noise shaping. The nextexample relates to the improved behavior as illustrated by FIG. 5. Acausal cross-band prediction as taught in the present document isobtained by selecting the prediction mask support B={(p−1, −1), (p, −1),(p+1, −1)}, which requires only one earlier time slot instead of two,and which performs a noise shaping with less alias frequencycontributions than the classical prediction mask 202 of the firstexample. The prediction mask support B={(p−1, −1), (p, −1), (p+1, −1)}corresponds to the prediction mask 203 of FIG. 2. The normal equations(26) based on the approximation of equation (25) reduce in this case totwo equations for the three unknown coefficients c_(m)[−1], m=p−1, p,p+1,

$\begin{matrix}{\begin{Bmatrix}{{{\hat{v}\left( {\pi\; f} \right)}{c_{p}\left\lbrack {- 1} \right\rbrack}} = {\left( {- 1} \right)^{p + 1}{\hat{v}\left( {\pi\; f} \right)}{\sin\left( {\pi\; f} \right)}}} \\{{{{\hat{v}\left( {\pi\left( {f + 1} \right)} \right)}{c_{p - 1}\left\lbrack {- 1} \right\rbrack}} - {{\hat{v}\left\lbrack {\pi\left( {f - 1} \right)} \right)}{c_{p + 1}\left\lbrack {- 1} \right\rbrack}}} = {\left( {- 1} \right)^{p}{\hat{v}\left( {\pi\; f} \right)}{\cos\left( {\pi\; f} \right)}}}\end{Bmatrix}.} & (28)\end{matrix}$

One finds that any solution to equations (28) leads to a vanishing meanvalue of the squared prediction error according to equation (6). Apossible strategy to select one solution among the infinite number ofsolutions to equations (28) is to minimize the sum of squares of theprediction coefficients. This leads to the coefficients given by

$\begin{matrix}{\begin{Bmatrix}{{c_{p - 1}\left\lbrack {- 1} \right\rbrack} = \frac{\left( {- 1} \right)^{p}{\hat{v}\left( {\pi\; f} \right)}{\hat{v}\left( {\pi\;\left( {f + 1} \right)} \right)}{\cos\left( {\pi\; f} \right)}}{{\hat{v}\left( {\pi\left( {f - 1} \right)} \right)}^{2} + {\hat{v}\left( {\pi\left( {f + 1} \right)} \right)}^{2}}} \\{{c_{p}\left\lbrack {- 1} \right\rbrack} = {\left( {- 1} \right)^{p + 1}{\sin\left( {\pi\; f} \right)}}} \\{{c_{p + 1}\left\lbrack {- 1} \right\rbrack} = \frac{\left( {- 1} \right)^{p + 1}{\hat{v}\left( {\pi\; f} \right)}{\hat{v}\left( {\pi\left( {f - 1} \right)} \right)}{\cos\left( {\pi\; f} \right)}}{{\hat{v}\left( {\pi\left( {f - 1} \right)} \right)}^{2} + {\hat{v}\left( {\pi\left( {f + 1} \right)} \right)}^{2}}}\end{Bmatrix}.} & (29)\end{matrix}$

It is clear from the formulas (29) that the prediction coefficients onlydepend on the normalized frequency f with respect to the midpoint of thetarget subband p, and further depend on the parity of the target subbandp.

By using the same prediction mask support B={(p−1, −1), (p, −1), (p+1,−1)} to predict the three subband samples

x,w_(m,0)

for m=p−1, p, p+1, as illustrated by the prediction mask 204 of FIG. 2,a 3×3 prediction matrix is obtained. Upon introduction of a more naturalstrategy for avoiding the ambiguity in the normal equations, namely byinserting the relaxed sinusoidal model r(τ)=exp(−ε|τ|)cos(ξτ)corresponding to P(ω)=ε((ε²+(ω−ξ)²)⁻¹+(ε²+(ω+ξ)²)⁻¹), numericalcomputations lead to the 3×3 prediction matrix elements of FIG. 3. Theprediction matrix elements are shown as function of the normalizedfrequency

$f \in \left\lbrack {{- \frac{1}{2}},\frac{1}{2}} \right\rbrack$in the case of an overlap K=2 with a sinusoidal window functionν(t)=cos(πt/2) and in case of an odd subband p.

As such, it has been shown that signal models x(t) may be used todescribe underlying characteristics of the to-be-encoded input audiosignal. Parameters which describe the autocorrelation function r(τ) maybe transmitted to a decoder 100, thereby enabling the decoder 100 tocalculate the predictor from the transmitted parameters and from theknowledge of the signal model x(t). It has been shown that for modulatedfilterbanks, efficient means for determining the subband covariancematrix of the signal model and for solving the normal equations todetermine the predictor coefficients can be derived. In particular, ithas been shown that the resulting predictor coefficients are invariantto subband shifts and are typically only dependent on a normalizedfrequency relative to a particular subband. As a result, pre-determinedlook-up tables (as illustrated e.g. in FIG. 3) can be provided whichallow for the determination of predictor coefficients knowing anormalized frequency f which is independent (apart from a parity value)of the subband index p for which the predictor coefficients aredetermined

In the following, periodic model based prediction, e.g. using a singlefundamental frequency Ω, is described in further details. Theautocorrelation function r(τ) of such a periodic model is given byequation (13). The equivalent PSD or line spectrum is given by

$\begin{matrix}{{P(\omega)} = {\Omega{\sum\limits_{q \in Z}{{\delta\left( {\omega - {q\;\Omega}} \right)}.}}}} & (30)\end{matrix}$

When the period T of the periodic model is sufficiently small, e.g. T≦1,the fundamental frequency Ω=2π/T is sufficiently large to allow for theapplication of a sinusoidal model as derived above using the partialfrequency ξ=qΩ closest to the center frequency π(p+1/2) of the subband pof the target subband sample which is to be predicted. This means thatperiodic signals having a small period T, i.e. a period which is smallwith respect to the time stride of the filterbank, can be well modeledand predicted using the sinusoidal model described above.

When the period T is sufficiently large compared to the duration K ofthe filterbank window ν(t), the predictor reduces to an approximation ofa delay by T. As will be shown, the coefficients of this predictor canbe read directly from the waveform cross correlation function given byequation (19).

Insertion of the model according to equation (13) into equation (22)leads to

$\begin{matrix}{{{R_{n,m}\lbrack\lambda\rbrack} = {\sum\limits_{q \in Z}{U_{n,m}\left( {{qT} - \lambda} \right)}}},} & (31)\end{matrix}$

An important observation is that if T≧2K, then at most one term ofequation (31) is nonzero for each λ since U_(n,m)(τ)=0 for |τ|>K. Bychoosing a prediction mask support B=I×J with time slot diameterD=|J|≦T−K one observes that (n,k), (m,l) ∈B implies |k−l|≦T−K, andtherefore the single term of equation (31) is that for q=0. It followsthat R_(n,m)[k−l]=U_(n,m)(k−l), which is the inner product of orthogonalwaveforms and which vanishes unless both n=m and k=1. All in all, thenormal equations (7) becomec _(n)[k]=R _(n,p)[k],(n,k)∈B.  (32)

The prediction mask support may be chosen to be centered around k=k₀≈−T,in which case the right hand side of equation (32) has its singlecontribution from q=−1. Then the coefficients are given byc _(n)[k]=U_(n,p)[−k−T], (n,k)∈B,  (33)wherein the explicit expression from equation (19) can be inserted. Thegeometry of the prediction mask support for this case could have theappearance of the prediction mask support of the prediction mask 205 ofFIG. 2. The mean value of the squared prediction error given by equation(6) is equal to the squared norm of the projection of u_(p) (t+T) ontothe space spanned by the complement of the approximating waveformsw_(m,l)(t), (m,l)∉B.

In view of the above, it is taught by the present document that thesubband sample

x,w_(p,0)

from subband p and at time index 0) can be predicted by using a suitableprediction mask support B centered around (p,−T) with time diameterapproximately equal to T. The normal equations may be solved for eachvalue of T and p. In other words, for each periodicity T of an inputaudio signal and for each subband p, the prediction coefficients for agiven prediction mask support B may be determined using the normalequations (33).

With a large number of subbands p and a wide range of periods T, adirect tabulation of all predictor coefficients is not practical. But ina similar manner to the sinusoidal model, the modulation structure ofthe filterbank offers a significant reduction of the necessary tablesize, through the invariance property with respect to frequency shifts.It will typically be sufficient to study the shifted harmonic model withshift parameter −1/2<θ≦1/2 centered around the center of a subband p,i.e. centered around π(p+1/2), defined by the subset S(θ) of positivefrequencies among the collection of frequencies π(p+1/2)+(q+θ)Ω, q∈Z,

$\begin{matrix}{{P(\omega)} = {\Omega{\sum\limits_{\xi \in {S{(\theta)}}}{\left( {{\delta\left( {\omega - \xi} \right)} + {\delta\left( {\omega + \xi} \right)}} \right).}}}} & (34)\end{matrix}$

Indeed, given T and a sufficiently large subband index p, the periodicmodel according to equation (30) can be recovered with goodapproximation by the shifted model according to equation (34) by asuitable choice of the shift parameter θ. Insertion of equation (34)into equation (24) with n=p+ν and m=p+μ (wherein ν and μ define thesubband indexes around subband p of the prediction mask support) andmanipulations based on Fourier analysis leads to the followingexpression for the covariance matrix,

$\begin{matrix}{{R_{{p + v},{p + \mu}}\lbrack\lambda\rbrack} \approx {\frac{\left( {- 1} \right)^{p\;\lambda}}{2}{\sum\limits_{l \in Z}{{\kappa_{v - \mu}\left( {{Tl} - \lambda} \right)}{{\cos\left( {{2\;\pi\; l\;\theta} + {\frac{\pi}{2}\left( {{\left( {v + \mu} \right)\left( {\lambda - {Tl}} \right)} + \lambda - v + \mu} \right)}} \right)}.}}}}} & (35)\end{matrix}$

As can be seen, expression (35) depends on the target subband index ponly through the factor (−1)^(pλ). For the case of a large period T anda small temporal lag λ, only the term for l=0 contributes to expression(35), and one finds again that the covariance matrix is the identitymatrix. The right hand side of the normal equations (26) for a suitableprediction mask support B centered around (p, −T) then gives theprediction coefficients directly as

$\begin{matrix}{{{c_{p + v}\lbrack k\rbrack} = {\frac{\left( {- 1} \right)^{p\; k}}{2}{\kappa_{v}\left( {{- T} - k} \right)}{\cos\left( {{{- 2}\;\pi\;\theta} + {\frac{\pi}{2}\left( {{v\left( {k + T} \right)} + k - v} \right)}} \right)}}},\mspace{79mu}{\left( {{p + v},k} \right) \in {B.}}} & (36)\end{matrix}$

This recovers the contribution of the first term of equations (19) to(33) with the canonical choice of shift

$\theta = {{- {\pi\left( {p + \frac{1}{2}} \right)}}/{\Omega.}}$

Equation (36) allows determining the prediction coefficients c_(p+ν)[k]for a subband (p+ν) at a time index k, wherein the to-be-predictedsample is a sample from subband p at time index 0. As can be seen fromequation (36), the prediction coefficients c_(p+ν)[k] depend on thetarget subband index p only through the factor (−1)^(pk) which impactsthe sign of the prediction coefficient. The absolute value of theprediction coefficient is, however, independent of the target subbandindex p. On the other hand, the prediction coefficient c_(p+ν)[k] isdependent on the periodicity T and the shift parameter θ. Furthermore,the prediction coefficient c_(p+ν)[k] is dependent on ν and k, i.e. onthe prediction mask support B, used for predicting the target sample inthe target subband p.

In the present document, it is proposed to provide a look-up table whichallows to look-up a set of prediction coefficients c_(p+ν)[k] for apre-determined prediction mask support B. For a given prediction masksupport B, the look-up table provides a set of prediction coefficientsc_(p+ν)[k] for a pre-determined set of values of the periodicity T andvalues of the shift parameter θ. In order to limit the number of look-uptable entries, the number of pre-determined values of the periodicity Tand the number of pre-determined values of the shift parameter θ shouldbe limited. As can be seen from expression (36), a suitable quantizationstep size for the pre-determined values of periodicity T and shiftparameter θ should be dependent on the periodicity T. In particular, itcan be seen that for relatively large periodicities T (relative to theduration K of the window function), relatively large quantization stepsfor the periodicity T and for the shift parameter θ may be used. On theother extreme, for relatively small periodicities T tending towardszero, only one sinusoidal contribution has to be taken into account, sothe periodicity T loses its importance. On the other hand, the formulasfor sinusoidal prediction according to equation (29) require thenormalized absolute frequency shift

$f = {{\Omega\;{\theta/\pi}} = {\frac{1}{2}{\theta/T}}}$to be slowly varying, so the quantization step size for the shiftparameter θ should be scaled based on the periodicity T.

All in all, it is proposed in the present document to use a uniformquantization of the periodicity T with a fixed step size. The shiftparameter θ may also be quantized in a uniform manner, however, with astep size which is proportional to min(T, A), where the value of Adepends on the specifics of the filterbank window function. Moreover,for T<2, the range of shift parameters θ may be limited to |θ|≦min(CT,1/2) for some constant C, reflecting a limit on the absolute frequencyshifts f.

FIG. 6a illustrates an example of a resulting quantization grid in the(T, θ)-plane for λ=2. Only in the intermediate range ranging from0.25≦T≦1.5 the full two-dimensional dependence is considered, whereasthe essentially one-dimensional parameterizations as given by equations(29) and equations (36) can be used for the remaining range of interest.In particular, for periodicities T which tend towards zero (e.g. T<0.25)periodic model based prediction substantially corresponds to sinusoidalmodel based prediction, and the prediction coefficients may bedetermined using formulas (29). On the other hand, for periodicities Twhich substantially exceed the window duration K (e.g. T>1.5) the set ofprediction coefficients c_(p+ν)[k] using periodic model based predictionmay be determined using equation (36). This equation can bere-interpreted by means of the substitution θ=φ+1/4Tν. One finds that

$\begin{matrix}{{{c_{p + v}\lbrack k\rbrack} = {\frac{\left( {- 1} \right)^{p\; k}}{2}{\kappa_{v}\left( {{- T} - k} \right)}{\cos\left( {{{- 2}\;\pi\;\varphi} + {\frac{\pi}{2}\left( {{\left( {v + 1} \right)k} - v} \right)}} \right)}}},{\left( {{p + v},k} \right) \in {B.}}} & (37)\end{matrix}$

By giving φ the role given to the parameter θ in the tabulation, anessentially separable structure is obtained in the equivalent (T,φ)-plane. Up to sign changes depending on subband and time slot indices,the dependence on T is contained in a first slowly varying factor, andthe dependence on φ is contained in 1-periodic second factor in equation(37).

One can interpret the modified offset parameter φ as the shift of theharmonic series in units of the fundamental frequency as measured fromthe midpoint of the midpoints of the source and target bins. It isadvantageous to maintain this modified parameterization (T, φ) for allvalues of periodicities T since symmetries in equation (37) that areapparent with respect to simultaneous sign changes of φ and ν will holdin general and may be exploited in order to reduce table sizes.

As indicated above FIG. 6a depicts a two-dimensional quantization gridunderlying the tabulated data for a periodic model based predictorcalculation in a cosine modulated filterbank. The signal model is thatof a signal with period T 602, measured in units of the filterbank timestep. Equivalently, the model comprises the frequency lines of theinteger multiples, also known as partials, of the fundamental frequencycorresponding to the period T. For each target subband, the shiftparameter θ 601 indicates the distance of the closest partial to thecenter frequency measured in units of the fundamental frequency Ω. Theshift parameter θ 601 has a value between −0.5 and 0.5. The blackcrosses 603 of FIG. 6a illustrate an appropriate density of quantizationpoints for the tabulation of predictors with a high prediction gainbased on the periodic model. For large periods T (e.g. T>2), the grid isuniform. An increased density in the shift parameter θ is typicallyrequired as the period T decreases. However, in the region outside ofthe lines 604, the distance θ is greater than one frequency bin of thefilterbank, so most grid points in this region can be neglected. Thepolygon 605 delimits a region which suffices for a full tabulation. Inaddition to the sloped lines slightly outside of the lines 604, bordersat T=0.25 and T=1.5 are introduced. This is enabled by the fact thatsmall periods 602 can be treated as separate sinusoids, and thatpredictors for large periods 602 can be approximated by essentiallyone-dimensional tables depending mainly on the shift parameter θ, (or onthe modified shift parameter φ). For the embodiment illustrated in FIG.6a , the prediction mask support is typically similar to the predictionmask 205 of FIG. 2 for large periods T.

FIG. 6b illustrates periodic model based prediction in the case ofrelatively large periods T and in the case of relative small periods T.It can be seen from the upper diagram that for large periods T, i.e. forrelatively small fundamental frequencies Ω 613, the window function 612of the filterbank captures a relatively large number of lines or Diracpulses 616 of the PSD of the periodic signal. The Dirac pulses 616 arelocated at frequencies 610 ω=qΩ, with q ∈

. The center frequencies of the subbands of the filterbank are locatedat the frequencies

${\omega = {\pi\left( {p + \frac{1}{2}} \right)}},$with p ∈

. For a given subband p, the frequency location of the pulse 616 withfrequency ω=qΩ closest to the center frequency of the given subband

$\omega = {\pi\left( {p + \frac{1}{2}} \right)}$may be described in relative terms as

${{q\;\Omega} = {{\pi\left( {p + \frac{1}{2}} \right)} + {\Theta\;\Omega}}},$with the shift parameter Θ ranging from −0.5 to +0.5. As such, the termΘΩ reflects the distance (in frequency) from the center frequency

$\omega = {\pi\left( {p + \frac{1}{2}} \right)}$to the nearest frequency component 616 of the harmonic model. This isillustrated in the upper diagram of FIG. 6b where the center frequency617 is

$\omega = {\pi\left( {p + \frac{1}{2}} \right)}$and where the distance 618 ΘΩ is illustrated for the case of arelatively large period T. It can be seen that the shift parameter Θallows describing the entire harmonic series viewed from the perspectiveof the center of the subband p.

The lower diagram of FIG. 6b illustrates the case for relatively smallperiods T, i.e. for relatively large fundamental frequencies Ω 623,notably fundamental frequencies 623 which are greater than the width ofthe window 612. It can be seen that in such cases, a window function 612may only comprise a single pulse 626 of the periodic signal, such thatthe signal may be viewed as a sinusoidal signal within the window 612.This means that for relatively small periods T, the periodic model basedprediction scheme converges towards a sinusoidal modal based predictionscheme.

FIG. 6b also illustrates example prediction masks 611, 621 which may beused for the periodic model based prediction scheme and for thesinusoidal model based prediction scheme, respectively. The predictionmask 611 used for the periodic model based prediction scheme maycorrespond to the prediction mask 205 of FIG. 2 and may comprise theprediction mask support 614 for estimating the target subband sample615. The prediction mask 621 used for the sinusoidal model basedprediction scheme may correspond to the prediction mask 203 of FIG. 2and may comprise the prediction mask support 624 for estimating thetarget subband sample 625.

FIG. 7a illustrates an example encoding method 700 which involves modelbased subband prediction using a periodic model (comprising e.g. asingle fundamental frequency Ω). A frame of an input audio signal isconsidered. For this frame a periodicity T or a fundamental frequency Ωmay be determined (step 701). The audio encoder may comprise theelements of the decoder 100 illustrated in FIG. 1, in particular, theaudio encoder may comprise a predictor calculator 105 and a subbandpredictor 103. The periodicity T or the fundamental frequency Ω may bedetermined such that the mean value of the squared prediction errorsubband signals 111 according to equation (6) is reduced (e.g.minimized). By way of example, the audio encoder may apply a brute forceapproach which determines the prediction error subband signals 111 usingdifferent fundamental frequencies II and which determines thefundamental frequency Ω for which the mean value of the squaredprediction error subband signals 111 is reduced (e.g. minimized). Themethod proceeds in quantizing the resulting prediction error subbandsignals 111 (step 702). Furthermore, the method comprises the step ofgenerating 703 a bitstream comprising information indicative of thedetermined fundamental frequency Ω and of the quantized prediction errorsubband signals 111.

When determining the fundamental frequency Ω in step 701, the audioencoder may make use of the equations (36) and/or (29), in order todetermine the prediction coefficients for a particular fundamentalfrequency Ω. The set of possible fundamental frequencies Ω may belimited by the number of bits which are available for the transmissionof the information indicative of the determined fundamental frequency Ω.

It should be noted that the audio coding system may use a pre-determinedmodel (e.g. a periodic model comprising a single fundamental frequency Ωor any other of the models provided in the present document) and/or apre-determined prediction mask 202, 203, 204, 205. On the other hand,the audio coding system may be provided with further degrees of freedomby enabling the audio encoder to determine an appropriate model and/oran appropriate prediction mask for a to-be-encoded audio signal.

The information regarding the selected model and/or the selectedprediction mask is then encoded into the bit stream and provided to thecorresponding decoder 100.

FIG. 7b illustrates an example method 710 for decoding an audio signalwhich has been encoded using model based prediction. It is assumed thatthe decoder 100 is aware of the signal model and the prediction maskused by the encoder (either via the received bit stream or due topre-determined settings). Furthermore, it is assumed for illustrativepurposes that a periodic prediction model has been used. The decoder 100extracts information regarding the fundamental frequency Ω from thereceived bit stream (step 711). Using the information regarding thefundamental frequency Ω, the decoder 100 may determine the periodicityT. The fundamental frequency Ω and/or the periodicity T may be used todetermine a set of prediction coefficients for the different subbandpredictors (step 712). The subband predictors may be used to determineestimated subband signals (step 713) which are combined (step 714) withthe dequantized prediction error subband signals 111 to yield thedecoded subband signals 113. The decoded subband signals 113 may befiltered (step 715) using a synthesis filterbank 102, thereby yieldingthe decoded time domain audio signal 114.

The predictor calculator 105 may make use of the equations (36) and/or(29) for determining the prediction coefficients of the subbandpredictors 103 based on the received information regarding thefundamental frequency Ω (step 712). This may be performed in anefficient manner using a look-up table as illustrated in FIGS. 6a and 3.By way of example, the predictor calculator 105 may determine theperiodicity T and determine whether the periodicity lies below apre-determined lower threshold (e.g. T=0.25). If this is the case, asinusoidal model based prediction scheme is used. This means that basedon the received fundamental frequency Ω, the subbands p is determinedwhich comprises a multiple ω=qΩ, with q ∈

, of the fundamental frequency. Then the normalized frequency f isdetermined using the relation

${\xi = {\pi\left( {p + \frac{1}{2} + f} \right)}},$where the frequency ξ corresponds to the multiple ω=qΩ which lies insubband p. The predictor calculator 105 may then use equation (29) or apre-calculated look-up table to determine the set of predictioncoefficients (using e.g. the prediction mask 203 of FIG. 2 or theprediction mask 621 of FIG. 6b ).

It should be noted that a different set of prediction coefficients maybe determined for each subband. However, in case of a sinusoidal modelbased prediction scheme, a set of prediction coefficients is typicallyonly determined for the subbands p which are significantly affected by amultiple ω=qΩ, with q ∈

, of the fundamental frequency. For the other subbands, no predictioncoefficients are determined which means that the estimated subbandsignals 112 for such other subbands are zero.

In order to reduce the computation complexity of the decoder 100 (and ofthe encoder using the same predictor calculator 105), the predictorcalculator 105 may make use of a pre-determined look-up table whichprovides the set of prediction coefficients, subject to values for T andΘ. In particular, the predictor calculator 105 may make use of aplurality of look-up tables for a plurality of different values for T.Each of the plurality of look-up tables provides a different set ofprediction coefficients for a plurality of different values of the shiftparameter Θ.

In a practical implementation, a plurality of look-up tables may beprovided for different values of the period parameter T. By way ofexample, look-up tables may be provided for values of T in the range of0.25 and 2.5 (as illustrated in FIG. 6a ). The look-up tables may beprovided for a pre-determined granularity or step size of differentperiod parameters T. In an example implementation, the step size for thenormalized period parameter T is 1/16, and different look-up tables forthe quantized prediction coefficients are provided for T=8/32 up toT=80/32. Hence, a total of 37 different look-up tables may be provided.Each table may provide the quantized prediction coefficients as afunction of the shift parameter Θ or as a function of the modified shiftparameter φ. The look-up tables for T=8/32 up to T=80/32 may be used fora range which is augmented by half a step size,

$i.e.\mspace{14mu}\left\lbrack {\frac{9}{32},\frac{81}{32}} \right\rbrack.$For a given periodicity which differs from the available periodicities,for which a look-up tables has been defined, the look-up table for thenearest available periodicity may be used.

As outlined above, for long periods T (e.g. for periods T which exceedthe period for which a look-up table is defined), equation (36) may beused. Alternatively, for periods T which exceed the periods for whichlook-up tables have been defined, e.g. for periods T>81/32, the period Tmay be separated into an integer delay T_(i) and a residual delay T_(r),such that T=T+T_(r). The separation may be such that the residual delayT_(r) lies within the interval for which equation (36) is applicable andfor which look-up tables are available, e.g. within the interval [1.5,2.5] or [49/32, 81/32] for the example above. By doing this, theprediction coefficients can be determined using the loop-up table forthe residual delay T_(r) and the subband predictor 103 may operate on asubband buffer 104 which has been delayed by the integer delay T_(i).For example, if the period is T=3.7, the integer delay may be T_(i)=2,followed by a residual delay of T_(r)=1.7. The predictor may be appliedbased on the coefficients for T_(r)=1.7 on a signal buffer which isdelayed by (an additional) T_(i)=2.

The separation approach relies on the reasonable assumption that theextractor approximates a delay by T in the range of [1.5, 2.5] or[49/32, 81/32]. The advantage of the separation procedure compared tothe usage of equation (36) is that the prediction coefficients can bedetermined based on computationally efficient table look-up operations.

As outlined above, for short periods (T<0.25) equation (29) may be usedto determine the prediction coefficients. Alternatively, it may bebeneficial to make use of the (already available) look-up tables (inorder to reduce the computational complexity). It is observed that themodified shift parameter φ is limited to the range |φ|≦T with a samplingstep size of

${\Delta\;\varphi} = \frac{T}{32}$(for T<0.25, and for C=1, A=1/2).

It is proposed in the present document to reuse the look-up table forthe lowest period T=0.25, by means of a scaling of the modified shiftparameter φ with T_(l)/T, wherein T_(l) corresponds to the lowest periodfor which a look-up table is available (e.g. T_(l)=0.25). By way ofexample, with T=0.1 and φ=0.07, the table for T=0.25 may be queried witha rescaled shift parameter

$\varphi = {{\left( \frac{0.25}{0.1} \right) \cdot 0.07} = {0.175.}}$By doing this, the prediction coefficients for short periods (e.g.T<0.25) can also be determined in a computationally efficient mannerusing table look-up operations. Furthermore, the memory requirements forthe predictor can be reduced, as the number of look-up tables can bereduced.

In the present document, a model based subband prediction scheme hasbeen described. The model based subband prediction scheme enables anefficient description of subband predictors, i.e. a descriptionrequiring only a relatively low number of bits. As a result of anefficient description for subband predictors, cross-subband predictionschemes may be used which lead to reduced aliasing artifacts. Overall,this allows the provision of low bit rate audio coders using subbandprediction.

The invention claimed is:
 1. A method, performed by an audio signalprocessing device, for determining an estimate of a sample of a subbandsignal from two or more previous samples of the subband signal, whereinthe subband signal corresponds to one of a plurality of subbands of asubband-domain representation of an audio signal, the method comprisingdetermining signal model data comprising a model parameter; determininga first prediction coefficient to be applied to a first previous sampleof the subband signal; wherein a time slot of the first previous sampleimmediately precedes a time slot of the first sample; wherein the firstprediction coefficient is determined in response to the model parameterusing a first lookup table and/or a first analytical function;determining a second prediction coefficient to be applied to a secondprevious sample of the subband signal; wherein a time slot of the secondprevious sample immediately precedes a time slot of the first previoussample; wherein the second prediction coefficient is determined inresponse to the model parameter using a second lookup table and/or asecond analytical function; and determining the estimate of the sampleby applying the first prediction coefficient to the first previoussample and by applying the second prediction coefficient to the secondprevious sample; wherein the method is implemented, at least in part, byone or more processors of the audio signal processing device.
 2. Anaudio signal processing device configured to determine an estimate of asample of a subband signal from two or more previous samples of thesubband signal, wherein the subband signal corresponds to one of aplurality of subbands of a subband-domain representation of an audiosignal; wherein the audio signal processing device comprises a predictorcalculator configured to determine signal model data comprising a modelparameter; determine a first prediction coefficient to be applied to afirst previous sample of the subband signal; wherein a time slot of thefirst previous sample immediately precedes a time slot of the firstsample; wherein the first prediction coefficient is determined inresponse to the model parameter using a first lookup table and/or afirst analytical function; and determine a second prediction coefficientto be applied to a second previous sample of the subband signal; whereina time slot of the second previous sample immediately precedes a timeslot of the first previous sample; wherein the second predictioncoefficient is determined in response to the model parameter using asecond lookup table and/or a second analytical function; and a subbandpredictor configured to determine the estimate of the first sample byapplying the first prediction coefficient to the first previous sampleand by applying the second prediction coefficient to the second previoussample; wherein one or more of the predictor calculator and the subbandpredictor are implemented, at least in part, by one or more processorsof the audio signal processing device.
 3. A non-transitorycomputer-readable storage medium comprising a sequence of instructionswhich, when executed by a computer, cause the computer to perform themethod of claim 1.